Dropping missing values
A simple approach to handling missing values is to remove them completely. Some common approaches to removing missing values include listwise deletion and pairwise deletion. Listwise deletion involves removing all observations that contain one or more missing values. This approach is also known as complete-case analysis, meaning only complete cases are analyzed. On the other hand, in pairwise deletion, only the available data for each variable is used for analysis. Observations with missing values are included in the analysis. For each observation, in the variable where the missing value exists, the missing value is skipped/excluded, while for variables without the missing value, the value present is used for analysis. Pairwise deletion is also known as available case analysis.
Listwise deletion leads to loss of data, while pairwise retains data. Both methods can sometimes introduce bias into the dataset. The following diagram explains the difference between...
